{"id":"W3126245664","doi":"10.1021/acs.est.0c07309","title":"Electrocatalysis for Chemical and Fuel Production: Investigating Climate Change Mitigation Potential and Economic Feasibility","year":2021,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Electrocatalysts for Energy Conversion","field":"Energy","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"City University of Hong Kong","keywords":"Electrocatalyst; Biofuel; Environmental science; Life-cycle assessment; Greenhouse gas; Climate change; Renewable energy; Production (economics); Waste management; Chemistry; Engineering; Ecology; Electrochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008473268,0.0003678035,0.0003446857,0.0005965738,0.0003027098,0.0008467836,0.0003864909,0.0007833536,0.0009317713],"category_scores_gemma":[0.0004705599,0.0001623212,0.0005183751,0.0005983918,0.0003032838,0.0009120883,0.0004324573,0.0007769846,0.000215759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007679943,"about_ca_system_score_gemma":0.0005148202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001911089,"about_ca_topic_score_gemma":0.003274868,"domain_scores_codex":[0.9996163,0.00005025148,0.00001383563,0.00006579266,0.0001831399,0.00007065042],"domain_scores_gemma":[0.9998111,0.00005494538,0.0000168699,0.0000153954,0.00009119979,0.00001049516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002465591,0.0002913866,0.004924989,0.0005934985,0.00006582394,0.000212931,0.00007295246,0.007581061,0.9399131,0.003987703,0.0003442563,0.04176582],"study_design_scores_gemma":[0.00002234053,0.001154282,0.007459403,0.0000444654,0.00008025057,0.000184401,0.0002571739,0.02360674,0.9540488,0.001619529,0.01149582,0.0000266735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583372,0.01013187,0.0176273,0.0005772109,0.0000996184,0.0001663995,0.0004608215,0.00006945179,0.01253014],"genre_scores_gemma":[0.9874061,0.005209912,0.005270121,0.00008640624,0.00001352938,0.00005830091,0.0002304755,0.00001424614,0.001710831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001911089,"threshold_uncertainty_score":0.0055722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008850127921806554,"score_gpt":0.2186003626848219,"score_spread":0.2097502347630154,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}